摘要：With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials *** learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data *** with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel *** review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model ***,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and *** application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also ***,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.

摘要：Steels, accounting for a large proportion of metals and their alloys, are still irreplaceable structural materials in industrial applications for a long time. Classical dislocation theory sheds light that strength can be enhanced by impeding the dislocation ***, the ductility depends on the mobility of dislocation movement. As a result, one method of increasing strength or ductility always damages the other. In other words, there is a mutual exclusion between strength and ductility from the perspective of dislocation movement [1].

摘要：纳米晶在常温下具有很高的强度,然而其高温力学性能往往低于其对应粗晶。C是钢中的重要成分,由于其原子半径较小,容易在纳米晶中发生晶界偏析。通过分子动力学模拟,探讨了利用C的晶界偏析来提升纳米晶高温力学性能的可能性。在不同温度和应力水平下模拟了Fe-C与纯Fe纳米晶的拉伸蠕变试验,得到对应的应变速率,并根据Mukherjee-Bird-Dorn (MBD)公式得到了蠕变激活能和应力指数。通过对比Fe-C与Fe纳米晶的蠕变参数和缺陷结构,揭示了C晶界偏析影响α-Fe纳米晶塑性变形机制的影响机制。研究将有助于理解晶界偏析对不同温度下纳米晶力学性能的影响机制,启发优化合金高温力学性能设计的新思路。